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Created August 21, 2023 10:21
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"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install covsirphy pymannkendall"
],
"metadata": {
"id": "v9ZhJTbJlRoI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "Khu9zw8PErI_"
},
"outputs": [],
"source": [
"import covsirphy as cs\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"import matplotlib.patches as mpatches\n",
"import matplotlib.ticker as ticker\n",
"import matplotlib\n",
"\n",
"matplotlib.rcParams.update({'font.size': 14})\n",
"\n",
"import ruptures as rpt\n",
"import pymannkendall as mk\n",
"\n",
"import geopandas as gpd\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n"
]
},
{
"cell_type": "code",
"source": [
"country = \"USA\"\n",
"state = \"Illinois\"\n",
"county = \"Adams\"\n",
"start_date = None\n",
"end_date = \"30Apr2021\""
],
"metadata": {
"id": "k6QwP2t_ExkO"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"eng = cs.DataEngineer(['ISO3', 'Province', 'City'], complement=True)\n",
"eng.download(country=country, province=state, databases=[\"japan\", \"covid19dh\", \"owid\"])\n",
"eng.clean() # Cleans the fetched data\n",
"eng.transform() # Calculates Susceptible and other dependent variables"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f_QDIYGJFGeA",
"outputId": "a8499b34-9c53-4339-aee6-14310caa94cf"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<covsirphy.engineering.engineer.DataEngineer at 0x7f70d40a2440>"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"# Adds the layers based on the level of analysis\n",
"eng.layer(geo=(country, state, county), start_date=start_date, end_date=end_date)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 617
},
"id": "Uqj966_aFIQb",
"outputId": "be0f6a8c-070b-44d8-ac85-21821396c391"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ISO3 Province City Date Cancel_events Confirmed \\\n",
"0 USA Illinois Adams 2020-03-20 1.0 1.0 \n",
"1 USA Illinois Adams 2020-03-21 2.0 1.0 \n",
"2 USA Illinois Adams 2020-03-22 2.0 1.0 \n",
"3 USA Illinois Adams 2020-03-23 2.0 1.0 \n",
"4 USA Illinois Adams 2020-03-24 2.0 1.0 \n",
".. ... ... ... ... ... ... \n",
"402 USA Illinois Adams 2021-04-26 1.0 8273.0 \n",
"403 USA Illinois Adams 2021-04-27 1.0 8282.0 \n",
"404 USA Illinois Adams 2021-04-28 1.0 8303.0 \n",
"405 USA Illinois Adams 2021-04-29 1.0 8313.0 \n",
"406 USA Illinois Adams 2021-04-30 1.0 8322.0 \n",
"\n",
" Contact_tracing Country Fatal Gatherings_restrictions ... \\\n",
"0 1.0 United States 0.0 3.0 ... \n",
"1 1.0 United States 0.0 4.0 ... \n",
"2 1.0 United States 0.0 4.0 ... \n",
"3 1.0 United States 0.0 4.0 ... \n",
"4 1.0 United States 0.0 4.0 ... \n",
".. ... ... ... ... ... \n",
"402 1.0 United States 148.0 3.0 ... \n",
"403 1.0 United States 148.0 3.0 ... \n",
"404 1.0 United States 148.0 3.0 ... \n",
"405 1.0 United States 148.0 3.0 ... \n",
"406 1.0 United States 148.0 3.0 ... \n",
"\n",
" Population Recovered School_closing Stay_home_restrictions \\\n",
"0 65435.0 0.0 3.0 -1.0 \n",
"1 65435.0 0.0 3.0 2.0 \n",
"2 65435.0 0.0 3.0 2.0 \n",
"3 65435.0 0.0 3.0 2.0 \n",
"4 65435.0 0.0 3.0 2.0 \n",
".. ... ... ... ... \n",
"402 65435.0 0.0 1.0 1.0 \n",
"403 65435.0 0.0 1.0 1.0 \n",
"404 65435.0 0.0 1.0 1.0 \n",
"405 65435.0 0.0 1.0 1.0 \n",
"406 65435.0 0.0 1.0 1.0 \n",
"\n",
" Stringency_index Susceptible Testing_policy Tests Transport_closing \\\n",
"0 -55.56 65434.0 1.0 0.0 0.0 \n",
"1 -82.41 65434.0 1.0 0.0 0.0 \n",
"2 -82.41 65434.0 1.0 0.0 0.0 \n",
"3 -82.41 65434.0 1.0 0.0 0.0 \n",
"4 -82.41 65434.0 1.0 0.0 0.0 \n",
".. ... ... ... ... ... \n",
"402 -52.78 57162.0 3.0 0.0 0.0 \n",
"403 -52.78 57153.0 3.0 0.0 0.0 \n",
"404 -52.78 57132.0 3.0 0.0 0.0 \n",
"405 -52.78 57122.0 3.0 0.0 0.0 \n",
"406 -52.78 57113.0 3.0 0.0 0.0 \n",
"\n",
" Workplace_closing \n",
"0 1.0 \n",
"1 3.0 \n",
"2 3.0 \n",
"3 3.0 \n",
"4 3.0 \n",
".. ... \n",
"402 1.0 \n",
"403 1.0 \n",
"404 1.0 \n",
"405 1.0 \n",
"406 1.0 \n",
"\n",
"[407 rows x 24 columns]"
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" <td>1.0</td>\n",
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"</div>\n",
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},
"metadata": {},
"execution_count": 6
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]
},
{
"cell_type": "code",
"source": [
"actual_df, status, _ = eng.subset(geo=(country, state, county), variables=\"SIRF\", complement=True, start_date=start_date, end_date=end_date,)\n",
"print(status)\n",
"actual_df.tail()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 290
},
"id": "M3J1d2VIFK5k",
"outputId": "a03e8e4e-3a02-4c53-cdcb-e966d60fd9ff"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"monotonic increasing complemented confirmed data and \n",
"monotonic increasing complemented fatal data and \n",
"fully complemented recovered data\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Susceptible Infected Recovered Fatal\n",
"Date \n",
"2021-04-26 57162.0 150 7975 148\n",
"2021-04-27 57153.0 152 7982 148\n",
"2021-04-28 57132.0 173 7982 148\n",
"2021-04-29 57122.0 182 7983 148\n",
"2021-04-30 57113.0 191 7983 148"
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"metadata": {},
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},
{
"cell_type": "code",
"source": [
"# Create Dynamics to train the model\n",
"dyn_act = cs.Dynamics.from_data(model=cs.SIRDModel, data=actual_df, name=f\"{county} County\")\n",
"# Show registered values\n",
"dyn_act.register().tail()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "Zi5y6FYDFM_r",
"outputId": "624aa311-f1c4-446f-efcc-71441f739628"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Susceptible Infected Recovered Fatal kappa rho sigma\n",
"Date \n",
"2021-04-26 57162 150 7975 148 <NA> <NA> <NA>\n",
"2021-04-27 57153 152 7982 148 <NA> <NA> <NA>\n",
"2021-04-28 57132 173 7982 148 <NA> <NA> <NA>\n",
"2021-04-29 57122 182 7983 148 <NA> <NA> <NA>\n",
"2021-04-30 57113 191 7983 148 <NA> <NA> <NA>"
],
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" + ' to learn more about interactive tables.';\n",
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},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"# Show summary\n",
"dyn_act.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "PRVXYHGqFWRi",
"outputId": "e98fccc0-f30a-47c1-8bec-70e328e84d8f"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Start End\n",
"Phase \n",
"0th 2020-03-20 2021-04-30"
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" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-85b06c8a-eeb2-4a0e-8c4f-03306f4875dd button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-85b06c8a-eeb2-4a0e-8c4f-03306f4875dd');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-cc915687-e936-4fa4-a7f8-fb19f4c10433\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc915687-e936-4fa4-a7f8-fb19f4c10433')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-cc915687-e936-4fa4-a7f8-fb19f4c10433 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"actual_df[['Fatal']].plot()\n",
"(actual_df['Susceptible'] / 100).plot()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 586
},
"id": "YOqgDcpgFmX2",
"outputId": "6704940c-d777-4889-b62e-922759aa436b"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: xlabel='Date'>"
]
},
"metadata": {},
"execution_count": 10
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#Segment using the trend analysis\n",
"dyn_act.segment();\n",
"# Show summary\n",
"dyn_act.summary().T"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 673
},
"id": "_BJloX_AFbeU",
"outputId": "ae982c05-3c89-4c5d-a71c-79a8bb2576ea"
},
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
],
"image/png": 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\n"
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"Phase 0th 1st 2nd 3rd 4th 5th \\\n",
"Start 2020-03-20 2020-06-22 2020-07-12 2020-07-22 2020-08-02 2020-08-09 \n",
"End 2020-06-21 2020-07-11 2020-07-21 2020-08-01 2020-08-08 2020-08-15 \n",
"\n",
"Phase 6th 7th 8th 9th ... 24th 25th \\\n",
"Start 2020-08-16 2020-08-23 2020-09-01 2020-09-10 ... 2021-01-11 2021-01-21 \n",
"End 2020-08-22 2020-08-31 2020-09-09 2020-09-16 ... 2021-01-20 2021-01-31 \n",
"\n",
"Phase 26th 27th 28th 29th 30th 31st \\\n",
"Start 2021-02-01 2021-02-09 2021-02-16 2021-02-23 2021-03-02 2021-03-24 \n",
"End 2021-02-08 2021-02-15 2021-02-22 2021-03-01 2021-03-23 2021-04-05 \n",
"\n",
"Phase 32nd 33rd \n",
"Start 2021-04-06 2021-04-16 \n",
"End 2021-04-15 2021-04-30 \n",
"\n",
"[2 rows x 34 columns]"
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" <th>2nd</th>\n",
" <th>3rd</th>\n",
" <th>4th</th>\n",
" <th>5th</th>\n",
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" <th>8th</th>\n",
" <th>9th</th>\n",
" <th>...</th>\n",
" <th>24th</th>\n",
" <th>25th</th>\n",
" <th>26th</th>\n",
" <th>27th</th>\n",
" <th>28th</th>\n",
" <th>29th</th>\n",
" <th>30th</th>\n",
" <th>31st</th>\n",
" <th>32nd</th>\n",
" <th>33rd</th>\n",
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" <th>Start</th>\n",
" <td>2020-03-20</td>\n",
" <td>2020-06-22</td>\n",
" <td>2020-07-12</td>\n",
" <td>2020-07-22</td>\n",
" <td>2020-08-02</td>\n",
" <td>2020-08-09</td>\n",
" <td>2020-08-16</td>\n",
" <td>2020-08-23</td>\n",
" <td>2020-09-01</td>\n",
" <td>2020-09-10</td>\n",
" <td>...</td>\n",
" <td>2021-01-11</td>\n",
" <td>2021-01-21</td>\n",
" <td>2021-02-01</td>\n",
" <td>2021-02-09</td>\n",
" <td>2021-02-16</td>\n",
" <td>2021-02-23</td>\n",
" <td>2021-03-02</td>\n",
" <td>2021-03-24</td>\n",
" <td>2021-04-06</td>\n",
" <td>2021-04-16</td>\n",
" </tr>\n",
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" <th>End</th>\n",
" <td>2020-06-21</td>\n",
" <td>2020-07-11</td>\n",
" <td>2020-07-21</td>\n",
" <td>2020-08-01</td>\n",
" <td>2020-08-08</td>\n",
" <td>2020-08-15</td>\n",
" <td>2020-08-22</td>\n",
" <td>2020-08-31</td>\n",
" <td>2020-09-09</td>\n",
" <td>2020-09-16</td>\n",
" <td>...</td>\n",
" <td>2021-01-20</td>\n",
" <td>2021-01-31</td>\n",
" <td>2021-02-08</td>\n",
" <td>2021-02-15</td>\n",
" <td>2021-02-22</td>\n",
" <td>2021-03-01</td>\n",
" <td>2021-03-23</td>\n",
" <td>2021-04-05</td>\n",
" <td>2021-04-15</td>\n",
" <td>2021-04-30</td>\n",
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" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-bacf20b6-8391-4fe9-bbc1-2c9cafca1c67 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-bacf20b6-8391-4fe9-bbc1-2c9cafca1c67');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-7098fa88-1a47-4e3d-8cee-a820c1621412\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7098fa88-1a47-4e3d-8cee-a820c1621412')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-7098fa88-1a47-4e3d-8cee-a820c1621412 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"#Estimate the parameters for the scenario\n",
"dyn_act.estimate()\n",
"print(f\"Tau value [min]: {dyn_act.tau or 'un-set'}\")\n",
"# Show summary\n",
"dyn_act.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 410,
"referenced_widgets": [
"58afcc305093434cb7744425f3077433",
"65d39d3161454f55810d31fbe5c2b6fb",
"3ab1a76c3260478cad50c60425c7d615",
"1c0bbb38bf8243c59600b0555ebbc88e",
"cbc2bda4d1ec428d837195fab087859f",
"05335ad715c7440496c40a873cdcdb67",
"4f7e667732d847ebb357afa74b66ecaf",
"4f1660e67dce4e20bcb276c2acc7c616",
"a3df8216466543588827da5b95460c3d",
"6c4e4460fc134b558667c2728a7a3108",
"b55d6ec433f2419eb2b6d3e0798f700a"
]
},
"id": "r2RYm_IPFd5d",
"outputId": "8fcdf6ed-bd3a-4a4c-8724-28acb8ff5c31"
},
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/34 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "58afcc305093434cb7744425f3077433"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Tau value [min]: 480\n"
]
},
{
"output_type": "error",
"ename": "ZeroDivisionError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-56b958468c78>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Tau value [min]: {dyn_act.tau or 'un-set'}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Show summary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdyn_act\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/covsirphy/dynamics/dynamics.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;31m# Day parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tau\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m days_df = df[self._parameters].apply(\n\u001b[0m\u001b[1;32m 311\u001b[0m lambda x: np.nan if x.isna().any() else self._model.from_data(\n\u001b[1;32m 312\u001b[0m data=self._df.reset_index(), param_dict=x.to_dict(), tau=self._tau).dimensional_parameters(),\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[1;32m 9421\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9422\u001b[0m )\n\u001b[0;32m-> 9423\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9425\u001b[0m def applymap(\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 797\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 798\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 800\u001b[0m \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 812\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 815\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 816\u001b[0m \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/covsirphy/dynamics/dynamics.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 310\u001b[0m days_df = df[self._parameters].apply(\n\u001b[1;32m 311\u001b[0m lambda x: np.nan if x.isna().any() else self._model.from_data(\n\u001b[0;32m--> 312\u001b[0;31m data=self._df.reset_index(), param_dict=x.to_dict(), tau=self._tau).dimensional_parameters(),\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"expand\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m )\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/covsirphy/dynamics/sird.py\u001b[0m in \u001b[0;36mdimensional_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 155\u001b[0m }\n\u001b[1;32m 156\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mZeroDivisionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m raise ZeroDivisionError(\n\u001b[0m\u001b[1;32m 158\u001b[0m f\"Kappa, rho and sigma must be over 0 to calculate dimensional parameters with {self._NAME}.\") from None\n\u001b[1;32m 159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mZeroDivisionError\u001b[0m: Kappa, rho and sigma must be over 0 to calculate dimensional parameters with SIR-D Model."
]
}
]
},
{
"cell_type": "code",
"source": [
"est_df = dyn_act.estimate_params()\n",
"# Show RMSLE scores pf phases\n",
"est_df = est_df.drop_duplicates()\n",
"est_df.index.name = \"Start\"\n",
"display(est_df)"
],
"metadata": {
"id": "qxl1xUq8GgFG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"a2ad721ce07449c2b4715f35bade8e15",
"757a4a0b28f54128a5c7d0474591d4ea",
"0fbedb8906684e7297e9d5b53820f486",
"849d01a0600c4283ac3cc201370bc582",
"bf7ea20d140a4f31a04b9736d0c0259f",
"5b9bca732e5641a59b8b2d96d2ff8b47",
"c20c573d92844b828773824d85956a55",
"250cb42aa2da476d9c6e6027e729ea87",
"bd2e038837a54d669eef088b6766dab9",
"af986634f9054700baa6499034c34880",
"a8dda534168049409923eacb8ad29ff5"
]
},
"outputId": "46fd858d-4462-43dc-eaea-7783ce4e07c5"
},
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/34 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a2ad721ce07449c2b4715f35bade8e15"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" kappa rho sigma RMSLE Trials Runtime\n",
"Start \n",
"2020-06-22 0.0 0.045792 0.006164 0.099413 80 0 min 6 sec\n",
"2020-03-20 0.0 0.050244 0.041916 0.657238 71 0 min 7 sec\n",
"2020-07-22 0.000152 0.021998 0.013995 0.055282 113 0 min 7 sec\n",
"2020-07-12 0.0 0.035003 0.010822 0.027924 163 0 min 9 sec\n",
"2020-08-09 0.00086 0.02923 0.018628 0.045651 42 0 min 4 sec\n",
"2020-08-02 0.00095 0.025052 0.019827 0.05619 171 0 min 14 sec\n",
"2020-08-23 0.0 0.012934 0.023919 0.012619 22 0 min 1 sec\n",
"2020-08-16 0.0 0.015903 0.023714 0.012379 167 0 min 12 sec\n",
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"2020-09-17 0.0 0.036569 0.013245 0.019429 176 0 min 21 sec\n",
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"2020-10-11 0.000555 0.036906 0.027612 0.030515 68 0 min 8 sec\n",
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"2020-12-28 0.000326 0.041649 0.024426 0.057617 149 0 min 11 sec\n",
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"2021-01-11 0.001048 0.015579 0.024128 0.047268 103 0 min 9 sec\n",
"2021-01-21 0.000959 0.017358 0.026748 0.023227 88 0 min 6 sec\n",
"2021-02-01 0.000402 0.019632 0.029828 0.044992 37 0 min 2 sec\n",
"2021-02-09 0.000224 0.010564 0.024298 0.035507 105 0 min 9 sec\n",
"2021-02-16 0.000863 0.024538 0.031282 0.043294 85 0 min 8 sec\n",
"2021-03-02 0.0 0.000219 0.02065 0.07934 89 0 min 5 sec\n",
"2021-02-23 0.000315 0.013568 0.020444 0.029892 103 0 min 7 sec\n",
"2021-04-06 0.001481 0.047232 0.01322 0.070474 22 0 min 2 sec\n",
"2021-03-24 0.0 0.022465 0.008407 0.053211 65 0 min 6 sec\n",
"2021-04-16 0.0 0.024673 0.01544 0.038199 297 0 min 17 sec"
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},
{
"cell_type": "markdown",
"source": [
"## Additional analysis"
],
"metadata": {
"id": "kaXfSsoOkJZT"
}
},
{
"cell_type": "code",
"source": [
"pd.set_option(\"display.max_rows\", None)"
],
"metadata": {
"id": "VdQwy_pbm9AJ"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = dyn_act.register()\n",
"df[\"Fatal_diff\"] = df[\"Fatal\"].diff()\n",
"df.loc[df[\"kappa\"] == 0.0, [\"Fatal\", \"Fatal_diff\", \"kappa\"]]"
],
"metadata": {
"id": "MD7lhKDtG6ZK",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "2f08dd9e-9773-4f1f-a69c-d4be265b08d3"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Fatal Fatal_diff kappa\n",
"Date \n",
"2020-03-20 0 <NA> 0.0\n",
"2020-03-21 0 0 0.0\n",
"2020-03-22 0 0 0.0\n",
"2020-03-23 0 0 0.0\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-22</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-23</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-24</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-25</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-26</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-27</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-28</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-29</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-30</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-31</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-02</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-03</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-04</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-05</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-06</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-07</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-08</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-09</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-10</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-11</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-12</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-13</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-14</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-15</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-16</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-17</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-18</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-19</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-20</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-21</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-22</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-23</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-24</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-25</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-26</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-27</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-28</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-29</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-30</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-02</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-03</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-04</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-05</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-06</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-07</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-08</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-09</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-10</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-11</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-12</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-13</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-14</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-15</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-16</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-17</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-18</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-19</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-20</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-21</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-16</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-17</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-18</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-19</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-20</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-21</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-22</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-23</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-24</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-25</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-26</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-27</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-28</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-29</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-30</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-08-31</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-10</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-11</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-12</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-13</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-14</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-15</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-16</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-17</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-18</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-19</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-20</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-21</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-22</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-23</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-24</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-25</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-26</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-27</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-03</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-02</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-03</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-04</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-05</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-06</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-07</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-08</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-09</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-10</th>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-11</th>\n",
" <td>144</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-12</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-13</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-14</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-15</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-16</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-17</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-18</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-19</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-20</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-21</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-22</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-23</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-24</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-25</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-26</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-27</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-28</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-29</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-30</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-31</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-01</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-02</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-03</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-04</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-05</th>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-16</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-17</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-18</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-19</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-20</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-21</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-22</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-23</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-24</th>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-25</th>\n",
" <td>148</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-26</th>\n",
" <td>148</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-27</th>\n",
" <td>148</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-28</th>\n",
" <td>148</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-29</th>\n",
" <td>148</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-30</th>\n",
" <td>148</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61b5d5b3-b18e-4e49-85fa-097a8c64301f')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-61b5d5b3-b18e-4e49-85fa-097a8c64301f button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-61b5d5b3-b18e-4e49-85fa-097a8c64301f');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-c62dd71b-c958-4f77-a792-100b217056a3\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c62dd71b-c958-4f77-a792-100b217056a3')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-c62dd71b-c958-4f77-a792-100b217056a3 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 31
}
]
}
]
}
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